A Study of Machine Learning Based Stressed Speech Recognition System
International journal of intelligent engineering and systems · 2022
The nonverbal communication processes a critical parcel.In some cases verbal communication is incapable since the speaker does not utilize non-verbal communication well at the same time.Non-verbal communication which falls in unconscious emotion is important in determining in function of cognition, language comprehension, and decision making.However, a little research studied in this area.Many years, researchers are amazed by the reliability of Mel-Frequency Cepstral Coefficients (MFCC) feature extraction technique in recognizing stressed speech.In this paper, we propose a simple feature extraction technique that effective but strong enough to recognize stressed speech.There are the speech energy and frequency.We attempted a basic approach to classify unbiased or stretch on female and male discourse.The highlight extraction is based on control and recurrence.This investigate utilized 10 female and 10 male discourse datasets.There are 5 classification strategies utilized.The classification models are Neural Arrange, k-Nearest Neighbour, Bolster Vector Machine, combination of NN-k-NN and combination of NN-SVM.Test comes about approved utilizing k-fold cross-validation strategy.The tests are assessed utilizing R-index to distinguish whether the highlights contributing to the push discourse acknowledgment.Based on exploratory comes about, the number of inputs impacts the esteem of R-index.In general, combining the Neural Organize and Back Vector Machine is the most excellent classification strategy by appearing stretch acknowledgment rate of 85% precision.